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import gradio as gr
import openai
# OpenAI API Key (यहाँ अपनी API Key डालें)
openai.api_key = "YOUR_API_KEY"
# Backend Function: यूजर का मैसेज लेकर OpenAI से रिस्पॉन्स लाता है
def respond_to_message(message, chat_history):
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": message}]
)
bot_message = response.choices[0].message['content']
chat_history.append((message, bot_message))
return "", chat_history
# Frontend: Gradio UI
with gr.Blocks() as demo:
chatbot = gr.Chatbot(label="AI चैट बोर्ड")
msg = gr.Textbox(label="आपका मैसेज")
clear = gr.ClearButton([msg, chatbot])
msg.submit(respond_to_message, [msg, chatbot], [msg, chatbot])
demo.launch()
from datasets import load_dataset
# Login using e.g. `huggingface-cli login` to access this dataset
ds = load_dataset("KadamParth/NCERT_Chemistry_11th")
from transformers import OpenAIGPTTokenizer, TFOpenAIGPTModel
tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-community/openai-gpt")
model = TFOpenAIGPTModel.from_pretrained("openai-community/openai-gpt")
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